Google's former Chief Scientist explains why AI models are less important and why context engineering increasingly matters more.
The post Google’s Ex-AI Chief Jeff Dean Explains How To Improve Context Engineering appeared first on Search Engine Journal.
Microsoft has released code-testing-generator, an open-source agent for generating unit tests in any programming language, according to the company.
Released July 31, the code-testing-generator agent learns from the user’s repository, then plans, writes, and checks the tests to prove that they work. Currently the agent writes unit tests only. Integration tests, end-to-end tests, browser tests, and performance tests are outside its current scope, Microsoft said.
The agent coordinates test generation using the “research-plan-implement” (RPI) pipeline. First the agent searches the repository for the code that needs tests, detecting the language and test framework and looking for existing tests to guide its work, and finding the correct commands for building and running the tests.
Next the agent chooses the right amount of work from three paths:
The agent then plans and writes the tests It starts with simple code and then moves to code with more dependencies, mapping each behavior to a test file. The agent then checks that the generated tests are useful. According to Microsoft, it checks for the following problems before it finishes:
The code-testing-generator agent is available in the dotnet-test plugin in the dotnet/skills repository on GitHub. The plugin can be used in GitHub Copilot CLI and is also available in Visual Studio Code and VS Code Insiders through plugin support, which is a preview feature.
Impression share ran the auction years. Citation share runs this one. A 90-day sprint for measuring and winning AI search visibility.
The post I Helped Scale Google Ads To Billions – Here’s How I’d Build An AI Search Strategy Today appeared first on Search Engine Journal.
AI research habits are reshaping PPC accounts. Here is when to consolidate, when to segment, and how conversion values keep budget flowing to valuable buyers.
The post How AI Search Trends Are Changing PPC Campaign Structures – Ask A PPC appeared first on Search Engine Journal.
Sometimes you have to fight fire with fire. But when it comes to AI slop and hateful content threatening the safety and value of social media platforms, adding more fire—in this case, more AI—can make the problem worse.
At its best, social media can be a haven for people who want to share their experiences and knowledge. It gets closest to this ideal when users contribute authentic, valuable content, whether that’s a uniquely thoughtful blog post or a helpful video on how to build a PC. Relying primarily on AI tools to preserve that authenticity misses what makes social media worthwhile in the first place: the people behind it.
In April, a Slack channel for moderators of the r/AskHistorians Reddit community was usually busy. The channel, which automatically receives links to modmail messages, was flooded with alerts after dozens of comments and posts dating back 10 years were automatically removed from the subreddit.
© Getty
Agents have turned up just about everywhere in software this past year, with one conspicuous exception: data. That’s a little odd, because querying data is exactly the kind of structured, checkable task that agents excel at. The likeliest culprit is timing. Large language models have only been reliably good at writing SQL for the last six to nine months, and the field hasn’t caught up to what that unlocks. It’s worth separating two flavors of the idea: agents that do analytics, and agents that help you run the data plumbing. Both turn out to be more useful than they first look.
Data engineering is hard mostly because you’re at the mercy of systems you don’t control. Schemas change without warning. Sources go offline. The API you pull from ships a new version. A column that only ever holds integers starts returning decimals. A field you assumed was unique sprouts duplicates, and the next join detonates into a Cartesian explosion. Records go missing, or come back wrong for an hour and then quietly fix themselves. If nothing ever changed, data engineering would be easy. But as they say, the only constant is change.
Unglamorous maintenance is something agents are genuinely good at. Every data model is a stack of assumptions: this is unique, that’s always populated, these two tables join cleanly. An agent can read those assumptions out of your code and turn them into tests that check whether they still hold. A lot of the fixes are mechanical anyway: a table got renamed, a type got widened, a column moved. An agent can often patch those on its own, and when it can’t, it can still do the legwork, tracing what changed and handing a human a diagnosis and a proposed fix instead of just a 3am stack trace.
Context is the other half of the story, and the context landscape is honestly a mess. Vendors are working hard to convince you that only their semantic modeling language can save you, while it is not entirely clear whether these are necessary or even sufficient. Whether you keep your business logic in a semantic layer like MetricFlow or Malloy, or just in plain Markdown, the goal is the same: get that logic into a form an LLM can use. Context is almost always created by hand, and like all hand-written documentation, it starts drifting the moment it gets written down.
This highlights an opportunity, namely that agents are good at precisely the parts of context that are mechanical and bad at precisely the parts that aren’t. An agent can infer which tables join to which, what values a column tends to hold, what your sales regions are, and which tables people actually query. What it can’t infer is the stuff that was never really a data question: the right way to calculate revenue, what counts as a “customer,” when the fiscal year starts. Those aren’t facts hiding in the warehouse waiting to be found. They’re decisions, often business ones, that a person has to make. What an agent can do is flag the moment one of them quietly stops being true.
The flashier pitch, where agents surface insights you never asked for, is the one I’d bet on last. It sounds wonderful to have hands-free analytics. An agent will keep watch over your data, notice what matters, and drop a dashboard tailored to whatever is happening today. But the bar is high for relevance and false positives can make human users lose confidence.
Deterministic alerting systems have the same problem. People end up turning off alarms because they are too hard to tune. But if humans writing pre-canned triggers have a hard time getting it right, it is going to be hard for agents to do better (at least not before we get some form of super-intelligence). While I’d expect proactive insights to be part of the future, they are still a research prototype at this point.
Here are three concrete things a data team should do to get their stack ready for agents:
Latency matters more than you’d expect when you’re using agents. While you might be waiting seconds or minutes for Claude Code to do its thing, it is often running a bunch of tasks. Part of the time that the agent spends is waiting for the LLM, but an increasing amount of time is using other tools, like querying a database. Over time, you can expect LLMs to get a lot faster; you can use smaller models, smarter models, local models, or fancier GPUs. As that happens the tools that an agent uses become the bottleneck.
Picture two engines: one answers in 10 milliseconds, the other in 100. A person won’t notice the difference because both feel near instantaneous, and a person will spend far longer thinking up the next question than either engine spends answering it. What feels instantaneous to an agent is very different, and it doesn’t need to stop and think. When its next query depends on the last result, that 10x gap compounds straight into 10x more work per minute.
One of the ways to make an agent go faster is to take more of their work and run it in parallel. But this also increases load on the systems. You’d want to make sure you have enough parallel capacity and isolation to be able to scale to all of the parallel agent queries at once. Engines tuned for human patience and engines tuned for agent throughput are not the same engines.
The agentic wave is coming whether or not any given team is ready, and the best time to start preparing yourself and your stack is now, before the queries start pouring in. This isn’t just future proofing. The teams that move early are the ones who work out the patterns everyone else ends up copying. A little curiosity now buys a real head start later.
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New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.
Microsoft has unveiled a suite of IQ products over the last few months. Work IQ, Fabric IQ, and Foundry IQ build on what Microsoft used to call its “Graphs,” the underlying data architecture that underpins its cloud services. These graphs provided a way to query the data your business uses, treating that data as nodes in a graph database and using the GraphQL API model to extract that data — for example, to pull data related to a specific individual held across the various Microsoft 365 applications.
The IQ suite follows a similar approach, using the same data, but treating it as the sparse vector store needed to provide grounding data for LLM-based applications. By treating the data as a set of embedding vectors, and integrating it with Model Context Protocol (MCP) servers, Microsoft is giving you the necessary tools to tie LLM output to your data, reducing the risk of hallucination and improving accuracy. Using your own data is a key part of delivering effective agents, ensuring they work within your constraints.
Web IQ, the latest member of the IQ suite, was unveiled at Build 2026. A modernization of the retired Bing Search APIs, Web IQ is an agent-focused web search tool that builds on the massive Bing search index to provide up-to-date general information for use in your applications.
It may seem a little odd to be talking about a web-wide source of grounding data in the context of a suite of tools that exist to improve the accuracy of your AI applications by providing access to your Microsoft-hosted data. However, in many cases you want to link your agent not only to your data but also to related information from the wider world. For example, an agent powering an ecommerce service could use Web IQ and web-based data sources to provide product comparisons. An agent managing stock levels for a product that is weather-sensitive could use Web IQ as a source of weather data, using Bing’s multiple weather feeds and forecasts.
Just as Google Gemini drew on Google Search, Microsoft Copilot began by using Bing search data to provide grounding for consumer chatbots. It’s easy to take a service like Bing and use it with a LLM, as the nearest neighbor search algorithms use semantic vector similarity techniques to find results that look like your query, ranking them according to their proximity to your search terms.
Microsoft has been tuning its search vector index and the underlying technology stack to work with agents, as agents operate much differently than humans searching the web or querying a chatbot. Providing web search capabilities to agents means having to deal with persistent queries, as the agent hunts for the information it needs, refining queries and applying reasoning algorithms to develop the response it needs. LLM inferencing requires quick responses that deliver large amounts of data, working with queries that go far beyond the one-word or two-word requests that are typical of humans.
Using Web IQ gives you access to up-to-date information, beyond the training data used to build and weight an LLM. Bing’s crawler works within the standards developed by the search engine industry, obeying meta tags and using its own algorithms to crawl regularly updated websites more often. Bing’s crawler ensures that data is both fresh and being used appropriately, with a focus on quality rather than quantity.
Providing access to web data is only part of Web IQ. Microsoft is using Web IQ to host its own models to manage embeddings, ranking, and content extraction, all running on the company’s global hyperscale platform. The intent here is to use only a limited number of models, to keep the system performance high while aiming to deliver accurate results. The Web IQ models are different from those used to deliver search results to humans, as they’re designed to deliver responses that are suitable for LLMs to use for reasoning.
The underlying search system is based on the DiskANN algorithm developed by Microsoft Research, which allows fast search without requiring enormous amounts of in-memory data access. This approach has been extended to manage information retrieval at scale, building on Microsoft’s distributed systems architectures, to support the demands Microsoft is seeing from agent-based systems. At the same time, it must respond to the rapidly changing economics of inference, where token costs now demand the best possible output from the fewest tokens.
To meet those economic demands, the Web IQ platform doesn’t deliver whole documents to querying agents. Whole documents can lead to expensive inference further down the chain, as LLMs process results repeatedly to drive the agent workflow. Instead, Web IQ structures the information retrieved from the underlying search engine data, delivering what Microsoft calls “structured evidence objects” as well as passage-level information from unstructured text documents. This should result in a much higher signal-to-noise ratio than simply querying a search engine, with a focus on delivering information that lets agents work using fewer tokens.
The API for Web IQ is a standard REST call, delivering a request object to the Web IQ endpoint. Along with your API authorization key, you will send a query, a set of parameters that control the number of results returned, the language and region used, and the maximum size of the responses and the format used. Responses can be returned in text, HTML, or markdown formats, as well as extracted passages that are selected for context. All other options return the full document, so can be more expensive to use. Markdown is an interesting alternative, as it can be used as the basis for giving agents semantic memories.
Results include important contextual and citation information, including web page titles and URLs, as well as data about when the site was last crawled and how stale the underlying information is. This can be used to improve grounding and provide more information that can be included in formatted responses — much in the same way as Bing’s Copilot displays context in the form of footnotes in its responses.
Responses to video searches include text descriptions. If these aren’t provided as part of the original web content, they will be generated by an LLM. The same approach is used for image searches, with both offering the same contextual cues as the web search API. If you don’t care about the type of data being returned, you can choose a “classic search,” which will return text, images, video, and news.
Microsoft provides LLM-ready documentation for the Web IQ service, with an llms.txt file and an OpenAPI description. These allow AI tools to discover Web IQ capabilities and include them in workflows as part of autonomous operations, so that agents and other AI applications can implement grounding calls to Web IQ whenever user interactions require them. The API descriptions include errors as well as the structure of a standard 200 response.
As Web IQ is designed for use by modern agent frameworks, the Web IQ API is available through an MCP server. The Web IQ MCP server exposes tools that map to API calls: web, videos, news, and images. They also include a browse option, which lets you pull content from a target URL. The service can be configured with a standard JSON file and requires an API key to control access and manage billing. If your account doesn’t have access to a specific tool, then it won’t be available from inside the MCP server.
If you’re building an agent and you want to evaluate the Web IQ MCP server, it can be added to common coding agents, such as the GitHub Copilot CLI. You can then test it out using familiar tools and generate code that can be dropped into applications via your choice of development tooling. Queries sent to the Web IQ MCP server use the same syntax as REST calls, without having to construct the calls yourself. Working with the MCP server allows you to connect Web IQ to your choice of agent framework, relying on its built-in MCP methods to reduce the code and maintenance overhead.
Web IQ is not for human interactions; Microsoft provides an alternative “Grounding with Bing” service for chatbots. Instead, Web IQ is a tool for agents, providing necessary background information that helps keep results fresh and relevant. It’s easy to use, fast, and, above all, cheap, which makes it an ideal tool for modern inference platforms built around Microsoft Azure’s AI tooling.
Learn how Freshpet stands out in AI Overviews and why trust signals matter for brand recognition in AI search.
The post The Trust Signals That Get Your Brand Cited By AI Overviews, Just Like Freshpet [Webinar] appeared first on Search Engine Journal.
Google loses Jeff Dean, who helped build Google Search and AI, replaced by DeepMind's Demis Hassabis as Chief Scientist.
The post Google Chief Scientist Who Helped Build Modern Search And AI Steps Away appeared first on Search Engine Journal.
If you run CRO or SEO, the oldest mistake in your discipline just came back wearing a new referrer string. Here's the fix.
The post AI Referrals Are Recreating The Oldest Mistake In Conversion Optimization appeared first on Search Engine Journal.
Until recently, it was common for companies and organizations to engage in “tokenmaxxing” — that is, maximizing their use of AI. But with AI costs going up, companies are now looking to save money, a trend underscored by a recent Microsoft decision to limit AI use by its employees.
“As we ramp up our use of GitHub Copilot to achieve our goals, we all need to be mindful of how we consume tokens,” Microsoft Executive Vice President Jay Parikh wrote in an email to the company’s employees.
Starting now, each department at Microsoft will be allocated a certain pool of tokens, with usage then adjusted up or down as needed.
The change prompted concern among some employees. “It’s very telling that a company that has invested so much in AI and subsidized so much AI inference is now advising its own employees to cut back on spending,” an anonymous Microsoft employee said in a comment to 404 Media.
Search volume told you what people want to know. It no longer tells you whether they need you after AI answers. Click Worthiness does.
The post When AI Takes The Click, Click Worthiness Should Guide Your Strategy appeared first on Search Engine Journal.
Citations, sentiment, and LLM referral traffic make poor KPIs. Here are the AI visibility metrics that actually connect to leads, pipeline, and revenue.
The post AI Visibility Measurement: What To Track & What To Ignore appeared first on Search Engine Journal.
Reddit's Q2 call laid out the daily destination strategy and the participation problem standing in its way. What marketers should do now.
The post Reddit Wants To Be The Destination, Not Just The Source Behind Search & AI appeared first on Search Engine Journal.
AI agent orchestration platforms coordinate role-based and task-based AI agents, along with the tools, data, and people they depend on, into multistep workflows. These platforms are highly important for organizations scaling from handfuls to thousands of AI agents running in production.
Two open standards do the connective work: MCP (Model Context Protocol) gives agents governed access to tools and data, while A2A (Agent2Agent) lets agents discover and delegate to one another, including agents built on other platforms. The orchestration layer sits on top, adding the routing, shared state, guardrails, governance, security, and observability needed to run workflows that range from fully autonomous to human-in-the-loop.
AI orchestration platforms may be the hottest AI technology of the year. In researching this article, I identified more than 60 commercial and open source platforms that businesses can use as a control plane to manage work between AI agents, people, and automations.
Like data fabrics and automation platforms, I suspect enterprises will utilize more than one AI agent orchestration platform. Platforms are being released by hyperscalers and solution providers in enterprise SaaS, process automation, customer experience, data management, AIops, and IT infrastructure. Development-centric platforms include open source, commercial, and no-code integration solution providers.
Here are five considerations when reviewing AI agent orchestration platforms.
AI agent orchestration platforms are non-deterministic and leverage AI capabilities to coordinate responses and actions across AI agents. One area to evaluate is how administrators implement controls and guardrails over which AI agents can coordinate with others and under what circumstances. Additionally, platforms should also have controls on when and where people should be involved before taking action.
“CIOs should focus on how the AI orchestration platform clearly applies controls over autonomous decision-making,” says Heather Richards, global vice president of go-to-market strategy at Verint. “Ideally, the platform makes it easy to define who or what can take actions, how decisions are approved, and where accountability sits when something goes wrong. If orchestration doesn’t have built-in governance, visibility, and human override, it will scale risk faster than it scales value.”
Observable AI agents are primary capabilities for tracing how they interact and where decisions are made. But even more important is to review how platforms govern access to the context layer, which can include retrieval-augmented generation (RAG) for language models, knowledge graphs, and semantic layers.
“When evaluating an AI orchestration platform, organizations should consider whether governance and observability were built into the architecture from day one,” says Caitlin Schuman, director of AI strategy and customer innovation at Presidio. “A strong platform should make it clear what context is being used and should have a control layer that routes work across systems, agents, and humans.”
Deploying trustworthy AI agents is important for gaining employee adoption. Charles Crouchman, chief product officer at Redwood Software, suggests evaluating how an AI agent orchestration platform establishes trustworthy operations with enterprise resources. He recommends asking these five questions:
“Validating these answers moves you from disconnected AI reasoning to real execution, empowering you to take the next step towards an autonomous enterprise,” says Crouchman.
AI agent orchestration platforms centralize a growing number of operational workflows, so it’s important to evaluate whether their security, performance, reliability, and resiliency meet compliance and non-functional requirements.
“Deploying agents is the easy part; the hard part is ensuring they operate safely, consistently, and in coordination with the people and systems around them,” says Daniel Meyer, CTO at Camunda. “Orchestration platforms should enforce controls between an agent’s decision and its action, handle long-running processes without losing state, and maintain a full audit trail natively.”
Organizations should also consider how platforms support agentic ops practices for identity management, monitoring, AI agent accuracy, and incident management.
“Don’t just seek solutions that coordinate workflow or handle the life cycle of an agent; also seek solutions that get the answers agents need faster, with more accuracy, all while meeting essential security and compliance requirements,” says James Urquhart, field CTO and technology evangelist at Kamiwaza. “A platform that securely coordinates context gathering and result formulation across widely disparate infrastructures and data sources is essential, not only to the performance of AI in the enterprise, but also to its agility.”
Testing AI agents requires validating changes before deployment, just as with continuous testing for applications and APIs. But it also requires evaluating prompts, responses, and actions in production and ensuring that agents aren’t drifting from expected parameters or going rogue. One area in which AI agent orchestration platforms differ is how they support testing AI agents, monitoring them in production, and providing a centralized source of feedback to support accuracy improvements.
“When selecting an AI orchestration platform, don’t overlook where the software it produces actually gets tested and validated,” says Jean-Philippe LeBlanc, senior vice president of engineering at CircleCI. “AI can accelerate every stage of development, but without rigorous, automated validation integrated into the delivery pipeline, you’re compounding risk at the same rate you’re compounding velocity.”
Armando Franco, senior director of cloud and platform modernization at TEKsystems Global Services, says, “The criterion that actually matters is whether continuous outcome evaluation is a first-class capability of the platform itself, because without it, iteration speed collapses and the program stalls.”
MCP and A2A are two ways AI agent orchestration platforms support open standards and enable connecting to an ecosystem of agents. Many platforms also allow developers to select and replace the underlying AI models and to choose from a range of AI code-generation tools. These flexibilities ensure teams can optimize around performance, accuracy, compliance, costs, and other future considerations.
“When evaluating an AI orchestration platform, we look first at composability and interoperability,” says Rajesh Arora, chief data and analytics officer at Principal. “The real test is not how many features it offers today, but whether it can connect models, data sources, agentic solutions, and workflows in a way that adapts to our AI strategy, tech stack, and changing business needs.”
Other interoperability criteria to review include the platform’s AI agent cataloging capabilities, how permissions are configured dynamically, and whether prebuilt connectors are available for the required integrations.
Leaders recognize that AI is currently reshaping business more than driving transformation. To be successful, organizations require AI governance that keeps up with strategy and doesn’t lag too far behind. The same is true for AI agent orchestration platforms, so it’s important to review their release notes and road maps to see whether providers strike a reasonable balance between innovation and governance.
“The right orchestration platform provides a unified policy layer that follows work across agents, workflows, and AI tools, enabling your teams to build freely while IT and security maintain full visibility at the action and output levels,” says Brandon Sammut, chief people and AI transformation officer at Zapier. “If your governance can’t keep pace with how fast your people are building, you’ll either slow them down or lose sight of what they’re building.”
Since AI agent orchestration platforms are a new category, technology leaders should partner with their financial, legal, and compliance colleagues to assess vendor viability risks. In addition, reviewing customer adoption and support capabilities is important as top solution providers will continue to evolve their platforms.
“A mature provider offers both a stable platform and the customer support you’ll need, and with a large customer base, they’ve already hit countless edge cases that can smooth your own implementation,” says Hannes Hapke, director of the 575 Lab at Dataiku. “Assess maturity by looking at funding and financial backing, the clarity and consistency of their public road map, and the size and activity of their community. An engaged user base, active forums, and a healthy ecosystem of integrations all signal a provider that will still be standing when you scale.”
Many organizations are still early in adopting AI agents and transitioning proofs of concept into production. But for those deploying a growing number of AI agents across many platforms, selecting an AI agent orchestration platform enables scaling workflows, operations, and governance.
SnapLogic has introduced its new SnapGPT, an agentic assistant that helps enterprise teams plan, build, understand, and operate integrations through natural language.
Unveiled July 28 and described as the company’s most-significant advancement in SnapGPT since 2023, the new SnapGPT evolves from an AI-powered integration copilot into an agentic assistant for the integration life cycle, combining platform intelligence, integration-specific reasoning, and operational guidance to help teams move from business intent to production faster, according to SnapLogic. SnapGPT combines agentic planning, integration-specific reasoning, pipeline execution validation, and more than 15 years of enterprise integration expertise built into the SnapLogic Agentic Integration Platform, including platform intelligence, operational context, and proven integration patterns, the company said.
SnapGPT now helps teams across the entire integration life cycle:
The new SnapGPT is available as part of the SnapLogic Agent Integration Platform. In June SnapLogic introduced the SnapLogic MCP Builder, a template-based tool that generates Model Context Protocol servers from existing integrations.
In an open letter, OpenAI this week turned to the court of public opinion in its existential war against Apple with a public notice in which the company refutes the iPhone maker’s claims concerning wholesale use of confidential information.
You can detect the depth of enmity between both firms in OpenAI’s opening lines to its letter, which begins: “Apple is one of the greatest companies of all time,” and then moves swiftly into defending itself against company’s claims, while attempting to characterize Apple’s complaints as weak.
As reported elsewhere, Apple filed suit against OpenAI in the US District Court for the Northern District of California on July 10. The litigation names OpenAI Foundation, OpenAI Group PBC, io Products, Chang Liu (former senior systems electrical engineer), and Tang Yew Tan (former vice president of product design for iPhone and Apple Watch, now OpenAI’s chief hardware officer), and alleges breach of intellectual property agreement and misappropriation of trade secrets under the Defend Trade Secrets Act. The company has since then filed preservation orders to protect evidence.
Apple’s complaint is more detailed than that. Among many other things, it claims Tan allegedly directed job candidates still at Apple to bring “actual parts” to interviews for “show and tell” sessions. It also alleges Tan distributed an internal Apple document describing Apple’s own departure security protocols to new hires before they resigned. Liu is separately accused of failing to return an Apple laptop and using it to download confidential technical documents.
Now, OpenAI argues Apple’s trade-secret lawsuit is based on factual errors, poor communication and misleading claims. It says Apple mistakenly contacted the wrong OpenAI lawyer after confusing two people with the same surname, falsely claimed a phone call had occurred, then acknowledged both mistakes without raising the allegations later included in the lawsuit. (Apple says it sent OpenAI a warning letter back in February with no response, which undercuts OpenAI’s “we offered to resolve this before litigation” statement.)
OpenAI argues that Chang Liu was responding to requests from Apple colleagues seeking help locating Apple files, reflecting Apple’s own access-management failures rather than misconduct. It also claims Tan consistently instructed OpenAI staff not to seek or use competitors’ confidential information. OpenAI maintains it neither possesses nor wants Apple’s trade secrets, offered to resolve concerns before litigation, and finally argues that Apple’s request for a preliminary injunction is unnecessary and unsupported.
Will Apple see it the same way? That seems unlikely, in part due to the extent of the claimed infractions. Apple will point to the hundreds of former Apple employees now at OpenAI, including former Chief Designer Jony Ive. In doing so, it will likely argue that the remit of the case is not defined by erroneous legal correspondence, though that is probably seen as an error. Instead, the substantial claims it’s likely to focus on are that OpenAI has been engaged in a multi-front attempt to accumulate information pertaining to Apple and its design processes through recruitment and the way it recruits.
It’s feasible both arguments have some validity. OpenAI might be right in pointing out weaknesses in Apple’s own approach to internal communications in terms of secrecy. And Apple is correct in pointing out that OpenAI moved to abuse those vulnerabilities, weaknesses in its approach that have only been identified during OpenAI’s campaign to grab secrets.
While the Apple lawyer’s error in approaching OpenAI might be an unforced error that helps the AI firm cast doubt on Apple’s claims, it doesn’t necessarily invalidate them — and both sides believe themselves to be justified. Deciding which company is in the right will be a matter of law and not of public opinion.
To prove its position, OpenAI shared some correspondence. These communications do seem to show a failure at Apple to properly implement device management over employee accounts, including the claim that personal iMessage accounts are routinely used to share corporate correspondence. That may be true, and shouldn’t be – it’s an obvious weakness in corporate security.
At the same time, the correspondence also shows hints of job opportunities at OpenAI for and to a former colleague, which kind of proves part of Apple’s point in terms of steady employee poaching. “I can always give you some fun side projects,” one message said.
Of course, interaction between former colleagues is inevitable,. But at the level of seniority here, it feels plausible this could be in breach of any off-ramping arrangements reached between Apple and its former employees. No doubt, courts will decide that – though it does underline Apple’s claims that OpenAI instructed former Apple staffers about how to leave without reaching such agreements.
“This isn’t Apple getting it wrong. It is OpenAI getting caught with its hand in the hardware cookie jar and then writing a blog post about how the jar was left unlocked,” US tech thought leader Brian Roemmele wrote on X.
Ultimately, what comes next is up to Apple and OpenAI. The two companies might reach a deal out of court, or be forced into an agreement by the legal system. The existential nature of the rivalry suggests the latter, rather than former.
It is also very telling that OpenAI, which now has more than 400 former Apple employees on its teams, including many former designers, also claims: “Apple’s request for a preliminary injunction is both based on false information and completely unnecessary because we do not have, nor want, any of their trade secrets. We’re much more interested in building innovative products and technologies that push the frontier.”
While Apple hasn’t yet responded, it will be interesting to see whether whatever hardware OpenAI ships looks and behaves like any released or unreleased Apple products; the latter will now be able to bring details of its own historical project design decisions — and the people who made them — to court.
I think this case will play out over time. Perhaps the most interesting question is whether OpenAI has taken what it knows about Apple to create its own internal product design and development LLM models. Would that use be legitimate? It would, after all, not be the first time an AI company has trained its models on other people’s creative energy.
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After more than 30 years the cybersecurity field, Keith Jones recently realized that his role had changed, from being a single contributor to manager of a fairly large team. And this team was getting a lot accomplished — tasks that used to take up a huge chunk of his workday.
No, his company hadn’t hired a group of new employees to work under him. He simply accelerated his use of artificial intelligence tools. Now, instead of grinding through a lot of basic tasks, that work is done for him while he focuses on bigger-picture work.
“It really feels like I have a team behind the scenes, but what I have is Claude [Anthropic’s AI tool],” says Jones, who currently works as a cybersecurity researcher. “I’ve been thinking for the last several months about how much differently I work now than I did a year ago, when I would sit and write all the low-level stuff before I could get to the 10% of the good stuff I really wanted to focus on. Now I can sit back and say, ‘Give me three different ways to solve this problem.’”
Keith Jones, cybersecurity researcher
Keith Jones
Most people working in the technology field, like Jones, have had to figure out how best to work with AI. The technology has come on strong, with many companies making its use mandatory and actively evaluating whether employees are faster and more efficient because of it. And while it is boosting productivity and taking over the burden of repetitive, manual tasks, it’s also creating a new level of stress and a dizzying kind of mental exhaustion.
So what can tech workers do about the heavier mental load that comes with using AI, on top of escalating worries about the safety of their own jobs? AI users and industry analysts say there are specific ways to ease some of those burdens and prepare for a changing job market.
When it comes to working with LLM tools, a well-known issue is dealing with AI workslop and hallucinations. The slop is AI-generated output that is low-quality, buzzword-heavy, and generic. It also can refer to bloated, boilerplate code. Hallucinations are inaccurate or completely made-up results. AI routinely offers this messy or incorrect information with total confidence, giving users a false sense of security.
Using this bad data can lead to anything from minor software bugs to severe liabilities. “Don’t believe the machine is infallible,” says Craig Shue, computer science professor and department head at Worcester Polytechnic Institute (WPI). “That’s when bugs will start working in. There’s a lot of misinformation on the internet, and that could be what the LLM is grabbing and using.”
Craig Shue, computer science professor and department head at Worcester Polytechnic Institute
WPI
Here are ways to combat the problem:
Let’s face it: The great promise of AI is that it will take over repetitive, manual tasks, which will save you an incredible amount of time. What isn’t talked about as much is that it also can create a new workload — one that can be exhausting in a whole new way.
“Is AI saving people time? The short answer is yes,” says J.P. Gownder, vice president and principal analyst with Forrester Research. “But people also are being overwhelmed with overproduced things. Everyone wants to look busy and they’re producing more, but not necessarily better. Managers have to push back on that or it’s not really saving you time.”
In a multi-year study by Upwork, the largest online freelance marketplace, 77% of employees reported that AI had increased their workload. The report noted that a boost in productivity comes with a “significant emotional and relational cost,” with 88% of workers who saw the highest productivity gains also feeling burned out. And IDC’s Future of Work 2026 survey reported that 24% of IT workers report increased workload as a top AI concern.
Here are ways to combat the problem:
Using AI often necessitates a different kind of mental processing, changing what had been the natural pacing of your day and dramatically increasing context switching. Instead of simply building and testing, someone might be jumping back and forth between auditing, fact-checking, prompting, and re-prompting. To manage strain and protect your focus, new strategies are needed.
Published this past March in the Harvard Business Review, a study by Boston Consulting Group and the University of California, Riverside, surveyed 1,500 workers and coined the term “AI brain fry.” The researchers found that juggling multiple AI tools causes decision fatigue and increases errors.
How to combat the problem:
With companies regularly using AI-based applicant tracking systems to filter resumes, and AI actively shifting job responsibilities and skills requirements, the strategy for how you apply for roles and handle interviews is changing.
Leo Freitas, an analyst and research manager at IDC Research, says it’s critical for job applicants to show potential employers what they can do that machines cannot. “You need demonstrable achievements,” he adds. “It’s good to show highly human skills.”
Leo Freitas, analyst and research manager at IDC
IDC
How to combat the problem:
Teresa Hill, founder and leader of Anchor GTM
Teresa Hill
The anxiety echoing through the tech industry is tangible as companies reallocate corporate capital toward automation. While both Gownder and Freitas emphasize that there is far more fear than actual AI-driven layoffs, the shift in corporate spending is undeniably stoking job insecurity.
“There’s this apocalyptical view that AI will take everyone’s job in a few years,” says Freitas. “I don’t see that happening, but many things will change in the nature of how we work. I don’t think the world is going to end tomorrow. But it’s always good to do a self-assessment and look at whether AI can do what you’re doing now.”
How to combat the problem:
Anthropic has launched an investigation into what went wrong during a recent test of three models that left a trio of companies accidentally hacked.
The company was testing how well Claude Opus 4.7, Claude Mythos 5, and an internal test model could find hidden information about fictional companies in simulated networks. But because of a misunderstanding by one of Anthropic’s partners, the AI models gained access to the internet — and managed to find real companies with the same or similar names as the fictional ones.
As a result, three companies were actually hacked. Anthropic said it halted the tests on July 23, and the affected companies were notified four days later. So far, the company has received responses from two of the three companies, according to Reuters.
Anthropic is not alone when it comes to renegade models. An OpenAI agent recently went rogue and breached AI platform Hugging Bear and a customer of the cloud platform Modal Labs.
Alibaba on Monday introduced Qwen3.8-Max, its largest artificial intelligence model to date, expanding its enterprise AI portfolio with an open-weight model designed for software engineering, multimodal reasoning, and other knowledge-intensive business workloads.
In a blog post announcing the launch, Alibaba described Qwen3.8-Max as a 2.4-trillion-parameter mixture-of-experts (MoE) model that activates only about 95 billion parameters during inference.
The company said the architecture is intended to improve inference efficiency while supporting coding, reasoning and multimodal tasks, with open-weight versions scheduled for release next week through Alibaba Cloud’s Model Studio.
“We believe it’s one of the most powerful model available today, compatible to leading frontier AI models, second only to Fable 5,” Alibaba said in an X post.
Alibaba published internal test results comparing Qwen3.8-Max against Claude Opus 4.8, Claude Fable 5, and OpenAI’s GPT-5.6 Sol on coding benchmarks, including SWE-bench Pro and a proprietary evaluation the company calls NL2Repo-Bench.
The company said it evaluated competing models using each vendor’s own coding harness, Claude Code for Anthropic’s models and Codex for GPT-5.6 Sol, and reported the highest published score across available configurations for each rival.
Charlie Dai, vice president and principal analyst at Forrester, said the launch signals Alibaba is closing ground on proprietary leaders, though that isn’t the full picture.
“Alibaba is narrowing the gap, but the larger story is the rapid maturation of open-weight models,” Dai said. “Enterprises increasingly have credible alternatives to proprietary frontier models, particularly for software engineering, domain customization, sovereignty, and cost-sensitive deployments, where openness often matters as much as absolute model performance.”
Alibaba said it tested the model on three unsupervised, multi-day coding projects requiring it to take a task from an empty project folder to completion without human assistance, including one project the company said took 16 days to complete on its own.
Alibaba also highlighted enterprise applications across legal compliance, financial analysis, engineering design, quantitative research and multimodal content creation, saying the model is intended to complete entire business workflows rather than individual AI-assisted tasks.
Amit Jena, development manager for AI at Kanerika, said that the claim deserves more scrutiny than it has received.
“The claim worth examining is not the parameter count. Alibaba says the model completed a software engineering project in 16 days. That sentence has been reprinted everywhere and interrogated nowhere,” Jena said. “Sixteen days of what? How many times did a human step in? Did the output survive code review?”
Jena said the open-weight commitment itself should also be read carefully. “Publishing weights is a separate act from opening an API endpoint,” he said. “Until there is a repository, a licence and a model card, open-weight describes an intention.”
Alibaba’s mixture-of-experts architecture activates roughly 95 billion of the model’s 2.4 trillion parameters per request, a design the company says lowers inference costs.
Dai said that tradeoff now matters more to enterprise buyers than raw model size. “Inference efficiency now matters more than raw model size for most enterprises,” he said. “Activating only a fraction of total parameters can significantly reduce serving costs and infrastructure requirements, making frontier-class performance more accessible for production deployments where scalability, latency, and economics are often bigger concerns than benchmark leadership.”
Jena said efficiency gains matter less than an organization’s ability to actually test the model. “Efficiency stopped being the interesting question. The constraint that actually binds is evaluation throughput,” he said.
Nitish Tyagi, senior principal analyst at Gartner, said the significance of the release lies less in the parameter count than in what it signals about competitive pressure on AI deployment costs.
“Gartner has previously predicted that, without stronger cost controls, AI coding expenses could exceed the average developer’s salary,” Tyagi said. “The combination of open weights, a mixture-of-experts architecture, and a one-million-token context window represents a meaningful step toward making AI-augmented software development more economically viable.”
Tyagi cautioned that enterprises need to look beyond inference costs when weighing the model for production use.
“Many organizations outside China may be hesitant to rely on models hosted within China, leading them to deploy through hyperscalers or on-premises infrastructure, both of which introduce additional costs,” he said.
Open-weight models also typically lack the indemnification protections that come with commercial AI vendors, he said, meaning enterprises need their own security, governance, and code-scanning controls to catch copyright and intellectual property risks before production deployment.
Jena said the flagship model announced Monday may not be the one enterprises end up running.
“Qwen3.8-27B, announced alongside the flagship and almost entirely ignored in coverage,” is the more deployable option for most organizations, he said, since it can run on infrastructure they own and fine-tune on their own data.
Dai said enterprise leaders evaluating the release should prioritize transparency and total cost of ownership over headline figures. “The key question is whether Qwen3.8 delivers measurable business outcomes, enterprise-grade reliability, lower total cost of ownership, and options for digital sovereignty compared with competing models,” he said.
The article originally appeared on InfoWorld.
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